Session 3
Basics of Species Distribution Models (SDMs)
 |
Species distribution models are instrumental in predicting and
understanding the geographic ranges of species. In this session, we will
delve into the use of dismo, mgcv and
randomForest R packages, covering the basics of species
distribution modelling. This will be a quick run-though over the theory
and workflow of how to construct SDMs using observational data, and
useful resources to help you along the way. Participants will learn how
to construct predictive models and assess habitat suitability for
species.
Theory and Concept
Species Distribution Models (SDMs) are fundamental tools in ecology and conservation biology, aiming to understand and predict the spatial distribution of species based on environmental variables. At its core, SDMs are grounded in the ecological niche theory, which suggests that species occupy specific ecological niches characterised by environmental conditions such as temperature, water quality, and habitat features. By quantifying the relationship between species occurrence records and environmental variables through statistical modeling techniques, SDMs provide insights into the ecological requirements and habitat preferences of species.
These models typically utilise various algorithms, including MaxEnt, Random Forest, and Generalized Linear Models, to predict species distributions across geographic areas. We will quickly go through an example dataset, and how different model structures and algorithm can drastically change your predicted distribution. SDMs have diverse applications, from assessing the potential impacts of climate change on species distributions to informing conservation planning and management strategies. However, they also come with limitations and uncertainties, including data quality issues, assumptions about species-environment relationships, and challenges in extrapolating predictions to novel environmental conditions. Continuous refinement and integration of multiple data sources and modeling approaches are essential for improving the accuracy and reliability of SDMs, thereby enhancing their utility in addressing pressing ecological and conservation challenges. In this session, we will run though a quick example of how SDMs are created in R, and the complexities of their development and interpretation.
Here we will provide a quick tour of SDMs, but if you want a more in-depth look at what goes into building and refining a SDM, check out some of these online sources (SDM Intro,terra vignette and Ecocommons). There is also a wealth of literature on the subject that you can refer to, including this curated list of R packages that you can use to explore these analyses.
In this session we will use occurrence data on Oceanic Whitetip Sharks (Carcharhinus longimanus) from the Indian Ocean to model their distribution using five commonly used SDM algorithms. We will also finally quickly introduce a simple ensemble model workflow to show how you can integrate outputs from multiple algorithms to get better predictions of distribution. We have provided code to replicate the analysis and plotting, but as we will have limited time during this session, we dont expect everyone to run these code. You can run them on your own time to see how it works!
Basic steps of building SDMs
Data preperation
Occurrence data
Absence or Pseudo-absence data
Environmental predictors
Model fitting
Classification models (BRT, GBM, Random Forest)
